Consumers are increasingly using automated mechanisms to perform every day transactions. Kiosks exist to avoid enterprise personnel and lines. These automated kiosks allow consumers to perform transactions with an enterprise or agency with little to no human intervention. Individuals also perform transactions online and are capable of using their smart phones to check out of stores.
Consequently, information about a specific consumer and that consumer's preferences and habits are becoming harder and harder to ascertain. Recently, advancements have permitted a centralized consumer preference database. This allows preferences of any particular consumer to be captured from a variety of channels (in-person communication, cell communication, web communication, in-store communication, home phone communication, kiosk communication, and the like). Such advancement is significant because preferences of a consumer on one communication channel may different with that of another communication channel.
However, this centralized enterprise preference system is still mostly a collection system. That is, preferences gathered are made available across channel communications but the preferences are collected and not computed in real time. So, preferences are not capable of being learned or dynamically altered based on actions taken by consumers over one or more communication channels.
As a result and for the most part, preference processing is not truly up-to-date and is not presently capable of being altered based on actual customer behavior across communication channels with an enterprise.